{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、找到持仓增多的"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始连接数据库...\n",
      "开始查找上次数据...\n",
      "开始导出数据...\n",
      "导出数据到【csv/持有基金个数增多的股票2023-03-18.csv】成功，谢谢使用。\n",
      "导出数据到【csv/基金持有市值增多的股票2023-03-18.csv】成功，谢谢使用。\n",
      "                                            持有基金个数增多的股票                                             \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票名称</th>\n",
       "      <th>【2023-02-14】持有基金个数</th>\n",
       "      <th>【2023-03-18】持有基金个数</th>\n",
       "      <th>【2023-02-14】基金持有市值</th>\n",
       "      <th>【2023-03-18】基金持有市值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>贵州茅台</td>\n",
       "      <td>433</td>\n",
       "      <td>458</td>\n",
       "      <td>286.570922</td>\n",
       "      <td>324.221364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>宁德时代</td>\n",
       "      <td>465</td>\n",
       "      <td>488</td>\n",
       "      <td>189.504198</td>\n",
       "      <td>204.297868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>比亚迪</td>\n",
       "      <td>258</td>\n",
       "      <td>277</td>\n",
       "      <td>79.526613</td>\n",
       "      <td>86.237534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>药明康德</td>\n",
       "      <td>193</td>\n",
       "      <td>209</td>\n",
       "      <td>93.478080</td>\n",
       "      <td>100.679948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>五粮液</td>\n",
       "      <td>260</td>\n",
       "      <td>276</td>\n",
       "      <td>205.218135</td>\n",
       "      <td>217.511235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>中国平安</td>\n",
       "      <td>241</td>\n",
       "      <td>256</td>\n",
       "      <td>52.989367</td>\n",
       "      <td>56.178697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>泸州老窖</td>\n",
       "      <td>212</td>\n",
       "      <td>227</td>\n",
       "      <td>185.268639</td>\n",
       "      <td>186.247609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>东方财富</td>\n",
       "      <td>174</td>\n",
       "      <td>189</td>\n",
       "      <td>39.872120</td>\n",
       "      <td>41.142381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>美的集团</td>\n",
       "      <td>150</td>\n",
       "      <td>164</td>\n",
       "      <td>17.885581</td>\n",
       "      <td>19.090109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>隆基绿能</td>\n",
       "      <td>222</td>\n",
       "      <td>234</td>\n",
       "      <td>52.149954</td>\n",
       "      <td>55.518825</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   股票名称  【2023-02-14】持有基金个数  【2023-03-18】持有基金个数  【2023-02-14】基金持有市值  \\\n",
       "0  贵州茅台                 433                 458          286.570922   \n",
       "1  宁德时代                 465                 488          189.504198   \n",
       "2   比亚迪                 258                 277           79.526613   \n",
       "3  药明康德                 193                 209           93.478080   \n",
       "4   五粮液                 260                 276          205.218135   \n",
       "5  中国平安                 241                 256           52.989367   \n",
       "6  泸州老窖                 212                 227          185.268639   \n",
       "7  东方财富                 174                 189           39.872120   \n",
       "8  美的集团                 150                 164           17.885581   \n",
       "9  隆基绿能                 222                 234           52.149954   \n",
       "\n",
       "   【2023-03-18】基金持有市值  \n",
       "0          324.221364  \n",
       "1          204.297868  \n",
       "2           86.237534  \n",
       "3          100.679948  \n",
       "4          217.511235  \n",
       "5           56.178697  \n",
       "6          186.247609  \n",
       "7           41.142381  \n",
       "8           19.090109  \n",
       "9           55.518825  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                            基金持有市值增多的股票                                             \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票名称</th>\n",
       "      <th>【2023-02-14】持有基金个数</th>\n",
       "      <th>【2023-03-18】持有基金个数</th>\n",
       "      <th>【2023-02-14】基金持有市值</th>\n",
       "      <th>【2023-03-18】基金持有市值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>伊利股份</td>\n",
       "      <td>110</td>\n",
       "      <td>110</td>\n",
       "      <td>24.012529</td>\n",
       "      <td>40.993405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>山西汾酒</td>\n",
       "      <td>121</td>\n",
       "      <td>120</td>\n",
       "      <td>162.065656</td>\n",
       "      <td>169.681386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>四维图新</td>\n",
       "      <td>19</td>\n",
       "      <td>18</td>\n",
       "      <td>1.064809</td>\n",
       "      <td>4.897533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>东方电缆</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>5.005474</td>\n",
       "      <td>6.883575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>恒瑞医药</td>\n",
       "      <td>107</td>\n",
       "      <td>101</td>\n",
       "      <td>28.590061</td>\n",
       "      <td>29.396164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>国电南瑞</td>\n",
       "      <td>16</td>\n",
       "      <td>14</td>\n",
       "      <td>2.140762</td>\n",
       "      <td>2.772532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>牧原股份</td>\n",
       "      <td>50</td>\n",
       "      <td>50</td>\n",
       "      <td>13.440192</td>\n",
       "      <td>13.735412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>济川药业</td>\n",
       "      <td>11</td>\n",
       "      <td>10</td>\n",
       "      <td>1.295436</td>\n",
       "      <td>1.530458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>建发股份</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>1.633291</td>\n",
       "      <td>1.859304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>中国人寿</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>11.848307</td>\n",
       "      <td>12.062134</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   股票名称  【2023-02-14】持有基金个数  【2023-03-18】持有基金个数  【2023-02-14】基金持有市值  \\\n",
       "0  伊利股份                 110                 110           24.012529   \n",
       "1  山西汾酒                 121                 120          162.065656   \n",
       "2  四维图新                  19                  18            1.064809   \n",
       "3  东方电缆                  20                  20            5.005474   \n",
       "4  恒瑞医药                 107                 101           28.590061   \n",
       "5  国电南瑞                  16                  14            2.140762   \n",
       "6  牧原股份                  50                  50           13.440192   \n",
       "7  济川药业                  11                  10            1.295436   \n",
       "8  建发股份                  14                  14            1.633291   \n",
       "9  中国人寿                  26                  26           11.848307   \n",
       "\n",
       "   【2023-03-18】基金持有市值  \n",
       "0           40.993405  \n",
       "1          169.681386  \n",
       "2            4.897533  \n",
       "3            6.883575  \n",
       "4           29.396164  \n",
       "5            2.772532  \n",
       "6           13.735412  \n",
       "7            1.530458  \n",
       "8            1.859304  \n",
       "9           12.062134  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                           持有基金个数减少最多的股票                                            \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>股票名称</th>\n",
       "      <th>【2023-02-14】持有基金个数</th>\n",
       "      <th>【2023-03-18】持有基金个数</th>\n",
       "      <th>【2023-02-14】基金持有市值</th>\n",
       "      <th>【2023-03-18】基金持有市值</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>科士达</td>\n",
       "      <td>25</td>\n",
       "      <td>21</td>\n",
       "      <td>9.721137</td>\n",
       "      <td>5.072483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>禾迈股份</td>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "      <td>5.365353</td>\n",
       "      <td>2.949142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>科华数据</td>\n",
       "      <td>18</td>\n",
       "      <td>14</td>\n",
       "      <td>7.955289</td>\n",
       "      <td>3.690625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>锦浪科技</td>\n",
       "      <td>48</td>\n",
       "      <td>45</td>\n",
       "      <td>27.057667</td>\n",
       "      <td>21.894680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中国海油</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>4.298109</td>\n",
       "      <td>4.280917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>TCL中环</td>\n",
       "      <td>68</td>\n",
       "      <td>66</td>\n",
       "      <td>23.291584</td>\n",
       "      <td>23.291056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>天合光能</td>\n",
       "      <td>105</td>\n",
       "      <td>103</td>\n",
       "      <td>35.939452</td>\n",
       "      <td>35.939221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>闻泰科技</td>\n",
       "      <td>22</td>\n",
       "      <td>20</td>\n",
       "      <td>1.201314</td>\n",
       "      <td>1.201153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>韦尔股份</td>\n",
       "      <td>38</td>\n",
       "      <td>36</td>\n",
       "      <td>2.901028</td>\n",
       "      <td>2.900775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>菲利华</td>\n",
       "      <td>23</td>\n",
       "      <td>21</td>\n",
       "      <td>7.651079</td>\n",
       "      <td>7.643435</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    股票名称  【2023-02-14】持有基金个数  【2023-03-18】持有基金个数  【2023-02-14】基金持有市值  \\\n",
       "0    科士达                  25                  21            9.721137   \n",
       "1   禾迈股份                  14                  10            5.365353   \n",
       "2   科华数据                  18                  14            7.955289   \n",
       "3   锦浪科技                  48                  45           27.057667   \n",
       "4   中国海油                  13                  10            4.298109   \n",
       "5  TCL中环                  68                  66           23.291584   \n",
       "6   天合光能                 105                 103           35.939452   \n",
       "7   闻泰科技                  22                  20            1.201314   \n",
       "8   韦尔股份                  38                  36            2.901028   \n",
       "9    菲利华                  23                  21            7.651079   \n",
       "\n",
       "   【2023-03-18】基金持有市值  \n",
       "0            5.072483  \n",
       "1            2.949142  \n",
       "2            3.690625  \n",
       "3           21.894680  \n",
       "4            4.280917  \n",
       "5           23.291056  \n",
       "6           35.939221  \n",
       "7            1.201153  \n",
       "8            2.900775  \n",
       "9            7.643435  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display\n",
    "from dataModel import JJs, Stocks, db\n",
    "\n",
    "old_date = \"2023-02-14\"\n",
    "new_date = \"2023-03-18\"\n",
    "\n",
    "db.close()\n",
    "print(\"开始连接数据库...\")\n",
    "db.connect()\n",
    "sSelect = Stocks.select()\n",
    "print(\"开始查找上次数据...\")\n",
    "olds = sSelect.where((Stocks.date == old_date))\n",
    "aups = []\n",
    "bups = []\n",
    "downs = []\n",
    "yesterday = int(new_date.split(\"-\")[-1]) - 1\n",
    "yesterday = \"-\".join(new_date.split(\"-\")[:-1]) + f\"-{yesterday}\"\n",
    "for old in olds:\n",
    "    name = old.name\n",
    "    new = sSelect.where(\n",
    "        (Stocks.date == new_date or Stocks.date == yesterday),\n",
    "        (Stocks.name == name),\n",
    "        (Stocks.n >= 10),\n",
    "    )\n",
    "    if new:\n",
    "        new = new[0]\n",
    "        data = {\n",
    "            \"股票名称\": new.name,\n",
    "            f\"【{old_date}】持有基金个数\": old.n,\n",
    "            f\"【{new_date}】持有基金个数\": new.n,\n",
    "            f\"【{old_date}】基金持有市值\": old.money,\n",
    "            f\"【{new_date}】基金持有市值\": new.money,\n",
    "        }\n",
    "        if new.n > old.n:\n",
    "            aups.append(data)\n",
    "        elif round(new.money, 3) > round(old.money, 3):\n",
    "            bups.append(data)\n",
    "        else:\n",
    "            downs.append(data)\n",
    "\n",
    "\n",
    "def asort(stock):\n",
    "    \"\"\"获取排序的key，持有基金个数按数量绝对值差排序\"\"\"\n",
    "    return stock[f\"【{new_date}】持有基金个数\"] - stock[f\"【{old_date}】持有基金个数\"]\n",
    "\n",
    "\n",
    "def bsort(stock):\n",
    "    \"\"\"获取排序的key，基金持仓市值按比例排序\"\"\"\n",
    "    return stock[f\"【{new_date}】基金持有市值\"] - stock[f\"【{old_date}】基金持有市值\"]\n",
    "\n",
    "\n",
    "aups = sorted(aups, key=asort, reverse=True)\n",
    "bups = sorted(bups, key=bsort, reverse=True)\n",
    "downs = sorted(downs, key=asort)\n",
    "dfa = pd.DataFrame(aups)\n",
    "dfb = pd.DataFrame(bups)\n",
    "dfc = pd.DataFrame(downs)\n",
    "\n",
    "print(\"开始导出数据...\")\n",
    "patha = f\"csv/持有基金个数增多的股票{new_date}.csv\"\n",
    "dfa.to_csv(patha, encoding=\"utf-8\")\n",
    "print(f\"导出数据到【{patha}】成功，谢谢使用。\")\n",
    "pathb = f\"csv/基金持有市值增多的股票{new_date}.csv\"\n",
    "dfb.to_csv(pathb, encoding=\"utf-8\")\n",
    "print(f\"导出数据到【{pathb}】成功，谢谢使用。\")\n",
    "db.close()\n",
    "if len(dfa):\n",
    "    print(f\"{'持有基金个数增多的股票':^100}\")\n",
    "    display(dfa[:10])\n",
    "else:\n",
    "    print(f\"{'好惨，没有基金持有个数增多的股票':^100}\")\n",
    "if len(dfb):\n",
    "    print(f\"{'基金持有市值增多的股票':^100}\")\n",
    "    display(dfb[:10])\n",
    "else:\n",
    "    print(f\"{'好惨，没有基金持有市值增多的股票':^100}\")\n",
    "print(f\"{'持有基金个数减少最多的股票':^100}\")\n",
    "display(dfc[:10])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 二、绘制股票的曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "开始查找股票数据...\n",
      "数据查找完毕，开始绘图...\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
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       "  </clipPath>\n",
       " </defs>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "from IPython.display import display\n",
    "from dataModel import JJs, Stocks, db\n",
    "\n",
    "#import matplotlib    \n",
    "#print(matplotlib.matplotlib_fname())# 获取mpl目录\n",
    "\n",
    "\n",
    "matplotlib.use(\"TkAgg\")  # 大小写无所谓 tkaGg ,TkAgg 都行\n",
    "plt.rcParams[\"font.family\"] = [\"sans-serif\"]  # 用来正常显示中文标签\n",
    "plt.rcParams[\"font.sans-serif\"] = [\"SimHei\"]  # 用来正常显示中文标签\n",
    "plt.rcParams[\"axes.unicode_minus\"] = False  # 用来正常显示负号\n",
    "%matplotlib inline\n",
    "\n",
    "name=\"贵州茅台\"\n",
    "\n",
    "sSelect = Stocks.select()\n",
    "print(\"开始查找股票数据...\")\n",
    "dates=[]\n",
    "datas = sSelect.where((name == Stocks.name))\n",
    "datas_dict={\n",
    "    '持有基金个数':[],\n",
    "    '持有基金市值':[],\n",
    "}\n",
    "for stock in datas:\n",
    "    stock:Stocks\n",
    "    dates.append(pd.to_datetime(stock.date))\n",
    "    datas_dict['持有基金个数'].append(stock.n)\n",
    "    datas_dict['持有基金市值'].append(stock.money)\n",
    "\n",
    "df = pd.DataFrame(datas_dict)\n",
    "df.index=dates\n",
    "print(\"数据查找完毕，开始绘图...\")\n",
    "\n",
    "fig = sns.lineplot(data=df)\n",
    "fig.get_figure().set_figwidth(12)  # 设置宽度\n",
    "fig.get_figure().set_figheight(8)  # 设置高度\n",
    "plt.title(f\"{name}基金持仓变化\")\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  }
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